Audio Encoder

Audio encoders are neural networks designed to transform raw audio waveforms into meaningful numerical representations, facilitating various downstream tasks like speech recognition, sound classification, and audio-visual integration. Current research emphasizes self-supervised learning techniques, often employing masked autoencoders or contrastive learning, to train robust encoders on massive, diverse audio datasets, including multi-channel and low-resource scenarios. These advancements are improving the accuracy and generalizability of audio processing systems across diverse applications, from real-time speech enhancement to more nuanced tasks like audio-guided image manipulation and semantic audio decomposition.